High-throughput non-destructive biomass determination during early plant development in maize under field conditions
High-throughput non-destructive biomass determination during early plant development in maize under field conditions
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DOI：
10.1016/j.fcr.2010.12.017
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发表时间：
2011-03-18
影响因子：
5.8
通讯作者：
Melchinger, A. E.
中科院分区：
文献类型：
--
作者：
Montes, J. M.;Technow, F.;Melchinger, A. E.
植物早期生长的育种、基因组和生理研究由于缺乏合适的工具来对田间试验中大量基因型的地上生物量进行非破坏性表型鉴定而受到阻碍。我们利用安装在拖拉机上的光幕(LC)和光谱反射(SR)传感器设计了一个高通量的表型检测平台，并在田间条件下对其性能进行了评估。本研究的目的是(I)使用不同的生物测定方法比较LC、SR传感器及其组合(LC圆加SR)的生物量测定，(Ii)评估校准数据集的组成对预测的平均相对误差(MRE)和验证决定系数(R-v(2))的影响，以及(Iii)评估生物量测定的重复性(w(2))。20个玉米品种在5个环境下进行了田间试验。传感器测量分三个阶段进行(第四、第六和第八叶的完全发育)。在记录了传感器的测量结果后，收获了一些地块来确定新鲜的生物量。生物量预测基于线性、非线性和局部加权多项式回归进行LC测量。采用偏最小二乘回归(PLSR)和支持向量机回归(SVMR)进行SR和LC环加SR测量。使用SVMR和全局抽样的LC循环加SR数据得到最低的MRE(0.11)和最高的R-v(2)(=0.97)。基于每个地块重复测量的重复性非常高。总之，本研究提供了一个概念验证，即基于LC和SR传感器的高通量、无损的表型检测平台在玉米和其他行作物田间试验的早期生物量测定中具有巨大的潜力。(C)2011爱思唯尔B.V.保留所有权利。
Breeding, genomic, and physiological research on early growth in plants is hampered by the lack of suitable tools for non-destructive phenotyping of the above-ground biomass of a large number of genotypes in field trials. We designed a high-throughput phenotyping platform employing light curtains (LC) and spectral reflectance (SR) sensors mounted on a tractor and evaluated its performance under field conditions. The objectives of our study were to (i) compare biomass determination by LC, SR sensors, and their combination (LC circle plus SR) using various biometric methods, (ii) evaluate the effect of the composition of the calibration data set on the mean relative error of prediction (MRE) and coefficient of determination of validation (R-v(2)), and (iii) assess the repeatability (w(2)) of biomass determination. Twenty maize genotypes were grown in field trials in five environments. Sensor measurements were taken at three stages (full development of the fourth, sixth and eighth leaf). After recording sensor measurements, plots were harvested to determine fresh biomass. Biomass prediction was based on linear, non-linear and locally weighted polynomial regression for LC measurements. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used for SR and LC circle plus SR measurements. The LC circle plus SR data using SVMR and global sampling resulted in the lowest MRE (0.11) and the highest R-v(2) (=0.97). Repeatability based on duplicate measurements of each plot was very high. In conclusion, this study provided a proof-of-concept that the described high-throughput, non-destructive phenotyping platform based on LC and SR sensors has a great potential for early biomass determination in field trials of maize and other row-crops. (C) 2011 Elsevier B.V. All rights reserved.